Experience-driven Lifelong Learning (ELL)

• ELL is a framework that enables AI agents to continuously acquire, consolidate, and internalize knowledge from real-world interactions.
• It emphasizes proactive experience exploration, persistent memory architectures, and modular skill learning to transform explicit experiences into implicit competencies.
• The framework integrates dynamic memory management and meta-cognitive review to mitigate catastrophic forgetting and support long-term adaptive performance.

Experience-driven Lifelong Learning (ELL) is a framework for building AI agents that continuously acquire, consolidate, and internalize knowledge from ongoing interaction with dynamic, real-world environments. Distinct from classical static-task learning, ELL emphasizes proactive experience collection, persistent memory, skill abstraction, and the internalization of explicit experiences into implicit, adaptable competencies. Agents governed by ELL principles are designed to self-evolve, refine practical skills, and autonomously adapt strategies across a diverse, interdependent set of tasks and environments (Cai et al., 26 Aug 2025).

1. Core Principles of Experience-driven Lifelong Learning

ELL is shaped by four foundational principles:

1. Experience Exploration: Agents engage in ongoing, self-motivated interactions with their environment, decomposing complex objectives into sequential decision-making trajectories. Each trajectory is an ordered tuple,

$$\xi = \langle o_0, a_0, r_0, \ldots, o_T, a_T, r_T \rangle$$

where $o_t$ are observations, $a_t$ actions, and $r_t$ rewards at timestep $t$. These trajectories build rich experiential datasets beyond passive or imitation-based learning.

1. Long-term Memory: ELL centralizes the role of a persistent, structured knowledge base $\mathcal{K}$ comprising:
   • Trajectory memories: Encoded experiential sequences.
   • Declarative knowledge: Explicit facts and domain knowledge accumulated over time.
   • Structural/relational knowledge: Networks of interrelated entities, events, or concepts. Operations such as Add, Update, Delete, and Combine enable dynamic knowledge organization and efficient retrieval for decision support.
2. Skill Learning: Agents autonomously abstract recurring patterns from experiential data, formulating them into modular, reusable skills. These skills are continuously refined through reflective review—performance is analyzed post hoc to reinforce effective strategies and amend suboptimal ones. Transfer and validation are performed by applying extracted procedures to novel, but related, tasks (Cai et al., 26 Aug 2025).
3. Knowledge Internalization: Explicit, discrete rules or memories are transformed into implicit, “second nature” routines for rapid execution in familiar contexts. Internalization bridges the gap between deliberative reasoning and fluent, context-sensitive action, and is critical for robust long-term competency.

2. Persistent Memory Architectures and Knowledge Organization

The structure, management, and indexing of memory are central in ELL:

• Structured Memory ($\mathcal{M}$): Includes hierarchical storage of experiences (trajectories, events, concepts), supporting fine-grained recall and compositionality.
• Memory Operations: Includes explicit mechanisms for adding, updating, deleting, and combining experiences, allowing temporal credit assignment and strategic forgetting, which are crucial for scalability and avoiding catastrophic forgetting.
• Skill Set ($\mathcal{F}$): Skills may be implemented as parameterized subroutines, scripts, or policy modules, validated via episodic memory or retrospective performance tracking.

Compared to classical context-based memory (short-lived, local to a session), the ELL memory is persistent, cross-episodic, and intended for transfer and reuse across a lifetime of experiences. The paradigm shift is thus from “Context” to “Memory” (Cai et al., 26 Aug 2025).

3. Learning Dynamics: Proactive Exploration and Skill Distillation

Unlike imitation learning—which relies on expert demonstrations or static datasets—ELL agents are characterized by self-directed exploration and proactive behavioral refinement:

• Exploration: Agents explore the state–action space via active decision-making, pursuing curiosity, novelty, or self-assigned subgoals.
• Feedback-driven Refinement: Action sequences are continuously evaluated, leading to experience-based updates in both declarative knowledge and procedural skill sets.
• Meta-cognition and Reflection: Agents engage in higher-order reasoning about their own performance, prompting reviews of both failures and successes for strategic adjustment. Knowledge distillation and scheduled fine-tuning phases are used to consolidate effective patterns into internal skills (Cai et al., 26 Aug 2025).
• Internalization Pipeline: Initially explicit knowledge is subject to practice-driven refinement and is eventually encoded as implicit responses, facilitating automatic adaptation and responsiveness.

4. StuLife Benchmark: Evaluating ELL in Open-ended Environments

To operationalize ELL and evaluate agent progress, the StuLife benchmark provides a simulated long-term, holistic educational environment modeling a student’s complete college journey:

• Three Modules:
  • In-Class Tasks: Emphasize foundational knowledge, skill acquisition, and structured learning.
  • Daily Campus Tasks: Require planning, resource management, and engagement in realistic, context-rich activities.
  • Examination Tasks: Test long-term memory retention, skill transfer, and adaptive performance under pressure.
• Long-horizon Trajectories: Tasks are interconnected, and the agent’s prior decisions affect future opportunities, mirroring real-world interdependencies and the need for persistent, transferable knowledge.
• Assessment Metrics: Evaluate memory retention, skill transfer, self-motivated behavior, and, crucially, the agent’s ability to synthesize and leverage experience for future generalization.

These features instantiate ELL’s prescribed paradigm shifts: from Imitation to Learning, Passive to Proactive agency, and ephemeral Context to enduring Memory (Cai et al., 26 Aug 2025).

5. Challenges: Scalability, Forgetting, and Internalization

ELL systems encounter several technical challenges:

• Catastrophic Forgetting: Without dynamic memory management and scheduled review, new experiences may overwrite prior knowledge. Replay strategies, memory partitioning, and skill validation phases are required to ensure knowledge continuity.
• Sparse/Delayed Rewards: Long-horizon or sparse-reward scenarios mandate sophisticated credit assignment schemes and persistent review to effectively internalize useful strategies.
• Memory Efficiency: The structured memory must support both low-latency retrieval and long-term storage; this motivates architectural choices such as multi-level memory (trajectories, concepts, relations), compressed representations, and database-inspired indexing.
• Knowledge/Skill Distillation: Effective routines must be distilled from discrete experiences without losing flexibility; periodic refinement (distillation, fine-tuning) and meta-cognitive triggers are required for robust internalization.

6. Context Engineering for Agent Self-Evolution

Context engineering refers to the deliberate design of the agent’s input, memory organization, and operational context to maximize long-term reasoning and adaptation:

• Prompting/History Structuring: Inputs are enriched with relevant episodic memory and dynamic summaries, allowing agents to weight past experience according to present context.
• Task and State Representation: Trajectories are encoded to enhance reusability and facilitate knowledge transfer.
• Scheduling of Reflection/Review: Automated periods for skill assessment and memory update promote autonomous self-evolution.
• Compensation for Statelessness: By leveraging persistent memory over transient context, agents mitigate architectural limitations and exhibit coherent behavior across episodes.

This approach transforms the agent from a reactive, stateless function approximator into a self-evolving, context- and memory-driven reasoner with long-term behavioral consistency (Cai et al., 26 Aug 2025).

7. Broader Implications and Outlook

ELL redefines the agent–environment interaction loop, emphasizing self-motivated, persistent, and skill-based learning. The framework is agnostic to the underlying model, compatible with both neural (RL, LLM-based) and symbolic agents, and applicable to domains demanding robust adaptation—education, robotics, multi-agent coordination, and open-world intelligence.

The introduction of benchmarks like StuLife advances the rigorous measurement of lifelong capability, while context engineering establishes a roadmap for the practical realization of AGI-level competence in self-evolving systems. Prospective advances might focus on meta-cognitive scaffolding, dynamic memory consolidation, and cross-domain skill transfer, advancing the theoretical and practical frontiers of experience-driven lifelong learning.